Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2025 Mar;79(3):108-116.
doi: 10.1111/pcn.13782. Epub 2025 Jan 15.

Neurobiological fingerprints of negative symptoms in schizophrenia identified by connectome-based modeling

Ziyang Gao #  1   2   3 , Yuan Xiao #  1   2   3 , Fei Zhu  1   2   3 , Bo Tao  1   2   3 , Qiannan Zhao  1   2   3 , Wei Yu  1   2   3 , Jeffrey R Bishop  4 , Qiyong Gong  1   2   3 , Su Lui  1   2   3
Affiliations

Neurobiological fingerprints of negative symptoms in schizophrenia identified by connectome-based modeling

Ziyang Gao et al. Psychiatry Clin Neurosci. 2025 Mar.

Abstract

Aim: As a central component of schizophrenia psychopathology, negative symptoms result in detrimental effects on long-term functional prognosis. However, the neurobiological mechanism underlying negative symptoms remains poorly understood, which limits the development of novel treatment interventions. This study aimed to identify the specific neural fingerprints of negative symptoms in schizophrenia.

Methods: Based on resting-state functional connectivity data obtained in a large sample (n = 132) of first-episode drug-naïve schizophrenia patients (DN-FES), connectome-based predictive modeling (CPM) with cross-validation was applied to identify functional networks that predict the severity of negative symptoms. The generalizability of identified networks was then validated in an independent sample of n = 40 DN-FES.

Results: A connectivity pattern significantly driving the prediction of negative symptoms (ρ = 0.28, MSE = 81.04, P = 0.012) was identified within and between networks implicated in motivation (medial frontal, subcortical, sensorimotor), cognition (default mode, frontoparietal, medial frontal) and error processing (medial frontal and cerebellum). The identified networks also predicted negative symptoms in the independent validation sample (ρ = 0.37, P = 0.018). Importantly, the predictive model was symptom-specific and robust considering the potential effects of demographic characteristics and validation strategies.

Conclusions: Our study discovers and validates a comprehensive network model as the unique neural substrates of negative symptoms in schizophrenia, which provides a novel and comprehensive perspective to the development of target treatment strategies for negative symptoms.

Keywords: connectome‐based predictive modeling; negative symptom; neural fingerprint; resting‐state functional connectivity; schizophrenia.

PubMed Disclaimer

References

    1. Owen MJ, Sawa A, Mortensen PB. Schizophrenia. Lancet (London, England) 2016; 388: 86–97.
    1. Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990‐2019: A systematic analysis for the global burden of disease study 2019. Lancet Psychiatry 2022; 9: 137–150.
    1. Galderisi S, Mucci A, Buchanan RW, Arango C. Negative symptoms of schizophrenia: New developments and unanswered research questions. Lancet Psychiatry 2018; 5: 664–677.
    1. Bobes J, Arango C, Garcia‐Garcia M, Rejas J. Prevalence of negative symptoms in outpatients with schizophrenia spectrum disorders treated with antipsychotics in routine clinical practice: Findings from the CLAMORS study. J. Clin. Psychiatry 2010; 71: 280–286.
    1. Rabinowitz J, Levine SZ, Garibaldi G, Bugarski‐Kirola D, Berardo CG, Kapur S. Negative symptoms have greater impact on functioning than positive symptoms in schizophrenia: Analysis of CATIE data. Schizophr. Res. 2012; 137: 147–150.